language:

• en license: apache-2.0 base_model: DexopT/Qwen3-4B-Cybersecurity tags:
• cybersecurity
• penetration-testing
• offensive-security
• infosec
• red-team
• abliteration
• uncensored
• heretic
• qwen3
• qwen
• unsloth
• fine-tuned
• text-generation datasets:
• DexopT/cyber_heretic
• DexopT/heretic-bad-prompts
• DexopT/heretic-good-prompts

Qwen3-4B-Cybersecurity-Heretic-16bit

Qwen3-4B-Cybersecurity with refusal directions removed via Heretic abliteration

Base Model Heretic GGUF Pass Rate License

🔵 Base version (with refusals): DexopT/Qwen3-4B-Cybersecurity

Model Description

Qwen3-4B-Cybersecurity-Heretic-16bit is DexopT/Qwen3-4B-Cybersecurity with refusal directions surgically removed using Heretic v1.2.0 — a technique that identifies and subtracts the model's "refusal direction" from its residual stream without retraining.

Refusal test results: 38/50 prompts answered (76% pass rate) using custom cybersecurity-specific bad/good prompt datasets.

Model Family

Abliteration Details

What is Heretic Abliteration?

Heretic computes the "refusal direction" — a vector in the model's residual stream that activates when the model encounters requests it refuses. It then uses LoRA to project out this direction, reducing refusals without retraining. Unlike jailbreaks, this modifies model weights directly.

Trial Selection

Trial 24 was selected from 30 Pareto-optimal trials balancing refusal reduction and model capability (KL divergence):

Datasets Used

Bad prompts cover: WiFi/WPA2 cracking, ransomware, rogue AP, XSS, WAF bypass, malicious macros. Good prompts cover: SQL injection, reverse shells, keyloggers, privilege escalation, AD attacks.

Usage

Transformers

from transformers import AutoModelForCausalLM, AutoTokenizer
import torch

model = AutoModelForCausalLM.from_pretrained(
    "DexopT/Qwen3-4B-Cybersecurity-Heretic-16bit",
    torch_dtype=torch.float16,
    device_map="auto",
)
tokenizer = AutoTokenizer.from_pretrained("DexopT/Qwen3-4B-Cybersecurity-Heretic-16bit")

messages = [
    {"role": "system", "content": "You are an expert cybersecurity assistant."},
    {"role": "user", "content": "Write a Python reverse shell payload."}
]

text = tokenizer.apply_chat_template(messages, tokenize=False, add_generation_prompt=True)
inputs = tokenizer(text, return_tensors="pt").to(model.device)

with torch.no_grad():
    outputs = model.generate(**inputs, max_new_tokens=512, temperature=0.7, top_p=0.8)

print(tokenizer.decode(outputs[0][inputs["input_ids"].shape[1]:], skip_special_tokens=True))

MLX (Apple Silicon)

pip install mlx-lm

mlx_lm.convert \
  --hf-path DexopT/Qwen3-4B-Cybersecurity-Heretic-16bit \
  --mlx-path ~/models/qwen3-heretic-mlx \
  --quantize --q-bits 8

mlx_lm.chat --model ~/models/qwen3-heretic-mlx

LM Studio / llama.cpp / Ollama

Use the GGUF version: DexopT/Qwen3-4B-Cybersecurity-GGUF

⚠️ Disclaimer

This model is intended for educational and research purposes only. The abliteration process reduces but does not eliminate all safety behaviors. Use responsibly and only on systems you have explicit permission to test. The authors are not responsible for any misuse.

Links